24 papers · ranked by Valyu relevance
Matthias K. Hoffmann, Amine Othmane, Kathrin Flaßkamp
Many engineering tasks require solving families of nonlinear constrained optimization problems, parametrized in setting-specific variables. This is computationally demanding, particularly, if solutions have to be computed across strongly varying parameter values, e.g., in real-time control or for model-based design.…
Wei Li, Yapeng Liu, Xiang Li, Bowen Deng + 4 more
Optimizing drilling parameters is essential for improving drilling efficiency and reducing operational costs in oil and gas engineering. This study presents an intelligent optimization approach for drilling parameters based on a hydraulic-mechanical specific energy (MSE) model. A time-series data fusion framework…
Marius Pille, Leon Martin, Emilius Richter, Dionysios Perdikis + 2 more
Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and…
Authors not listed
Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF)…
H. Haddad, Thibault Falque, P. Talbot, Pascal Bouvry
The performance of constraint programming solvers is highly sensitive to the choice of their hyperparameters. Manually finding the best solver configuration is a difficult, time-consuming task that typically requires expert knowledge. In this paper, we introduce probe and solve algorithm, a novel two-phase framework…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Hunter Dlugas, Jing Li, Xiang Zhang, Seongho Kim + 1 more
Highlights What are the main findings?1. • Differential evolution (DE) tuned spectral preprocessing parameters without predefined search spaces. 2. • Even within predefined parameter spaces, DE achieved modestly improved identification performance relative to grid search. 3. • DE provides a data-driven approach for…
Serena Landers, Sahil Pontula, Shiekh Zia Uddin, Sachin Vaidya + 2 more
We introduce the CLUSTER algorithm (\textbf{c}oordinate-\textbf{l}evel \textbf{u}pdate \textbf{s}trategy for \textbf{t}rust-region step \textbf{e}valuation \textbf{r}efinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
Doaa El-Nagar, Ibrahim Zeidan, Mohamed Issa
The Multi-Objective Sinh-Cosh Optimization Algorithm (MOSCHO) is presented in this article based on the memorized technique. MOSCHO is an extension version of the recently proposed Sinh-Cosh optimizer for multiple objective optimizations. The memorized local optimum is integrated with the global optimal solution to…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Maximilian Siska, Emma Pajak, Katrin Rosenthal, Antonio del Rio Chanona + 2 more
Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation. While still in its infancy, Bayesian optimization has recently…
Authors not listed
We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient…
Thomas Bartz-Beielstein
The `spotoptim` package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential Parameter Optimization (SPO) methodology, it provides a Kriging-based optimization loop with Expected Improvement, support for continuous, integer, and categorical…
Shijie Pan, Agustin Castellano, Zeyu Shen, Enrique Mallada
Learning-enabled decision systems often use offline data or computation to reduce online compute cost. Despite the empirical success of such approaches, there is limited general understanding of how much offline information is needed to achieve a desired accuracy under a fixed online computation budget. We study this…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Bao Tuan Chau, Yuma Miyai, Thomas D Roper
of Bayesian Optimization Utilizing Continuous Chemistry Digital Twins Authors: Bao Tuan Chau, Yuma Miyai, Thomas D Roper A novel chemistry optimization methodology has been developed and applied to digital twins for a nucleophilic aromatic substitution reaction and a two-step process to produce an intermediate for the…
Alaa A. K. Ismaeel, Ali M. El-Rifaie, Fatma A. Hashim, Kaiguang Wang + 3 more
Parameter identification of a proton exchange membrane fuel cell (PEMFC) involves estimating the unknown model parameters required to build an accurate predictive representation of fuel-cell performance using optimization-based techniques. Because these parameters are often unavailable in manufacturer datasheets, their…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
M. Paknahad, P. Hosseini, C. Paknahad, S. J. S. Hakim + 1 more
Engineering optimization problems are increasingly complex, requiring more sophisticated approaches to locate global optima. This paper presents the Adaptive Hybrid Optimization (AHO) algorithm, which addresses the limitations of single-equation update mechanisms and conventional linear decay schedules through three…
Marie Frederikke Garnæs, Rie Beck Olin, Pernille R. Jensen, Jan Henrik Ardenkjaer‐Larsen + 2 more
Hyperpolarized carbon-13 magnetic resonance has enabled the real-time observation of biochemical pathways in living cellular systems. Pharmacokinetic modeling of such experiments provides estimates of conversion rates between metabolites, which, in turn, can be used to distinguish between healthy and diseased tissues…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Xin-She Yang, Mehmet Karamanoglu
Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical experiments to show that the algorithm can work well in practice; however, in most cases…
Andrea Boscutti, Valeria Grasso, Tommaso Di Ianni
Low-intensity focused ultrasound (LIFU) is a promising neuromodulation modality, but challenges related to high response variability and the poorly understood parameter space undermine progress in clinical applications. To facilitate the development of therapeutic LIFU protocols, we developed an approach for…